Adaptive Auxiliary Particle Filter for Track-Before-Detect With Multiple Targets

Luis Ubeda-Medina; Angel F. Garcia-Fernandez; Jesus Grajal

doi:10.1109/TAES.2017.2691958

Adaptive Auxiliary Particle Filter for Track-Before-Detect With Multiple Targets

Luis Ubeda-Medina^*, Angel F. Garcia-Fernandez, Jesus Grajal

^*Corresponding author for this work

Department of Electrical Engineering and Automation

Polytechnic University of Madrid

Research output: Contribution to journal › Article › Scientific › peer-review

69 Citations (Scopus)

Abstract

A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.

Original language	English
Article number	7894277
Pages (from-to)	2317-2330
Number of pages	14
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	53
Issue number	5
DOIs	https://doi.org/10.1109/TAES.2017.2691958
Publication status	Published - Oct 2017
MoE publication type	A1 Journal article-refereed

Keywords

MULTITARGET TRACKING
CONVERGENCE RESULT
BAYESIAN-APPROACH

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10.1109/TAES.2017.2691958

Cite this

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title = "Adaptive Auxiliary Particle Filter for Track-Before-Detect With Multiple Targets",

abstract = "A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.",

keywords = "MULTITARGET TRACKING, CONVERGENCE RESULT, BAYESIAN-APPROACH",

author = "Luis Ubeda-Medina and Garcia-Fernandez, {Angel F.} and Jesus Grajal",

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AU - Garcia-Fernandez, Angel F.

AU - Grajal, Jesus

PY - 2017/10

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N2 - A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.

AB - A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.

KW - MULTITARGET TRACKING

KW - CONVERGENCE RESULT

KW - BAYESIAN-APPROACH

U2 - 10.1109/TAES.2017.2691958

DO - 10.1109/TAES.2017.2691958

M3 - Article

SN - 0018-9251

VL - 53

SP - 2317

EP - 2330

JO - IEEE Transactions on Aerospace and Electronic Systems

JF - IEEE Transactions on Aerospace and Electronic Systems

IS - 5

M1 - 7894277

ER -

Adaptive Auxiliary Particle Filter for Track-Before-Detect With Multiple Targets

Abstract

Keywords

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